1. Field of the Invention
The present invention relates to spoken dialog systems, and in particular to methods, systems, and machine-readable media for unsupervised learning in spoken dialog systems based on interactions for the respective spoken dialog systems.
2. Introduction
Statistical modeling methods that rely on learning from data rather than hand-crafted rules have become the preferred approach for the development of sophisticated automated speech recognition systems. These methods led to now mature automated speech recognition (ASR) technology that is becoming widely used in everyday applications. In recent years, the use of this methodology was extended from acoustic and language models to other components of a spoken dialog system (SDS), including spoken language understanding, semantic classification, and dialog management. Some of the components, such as, for example, a semantic classifier for call routing applications, matured beyond research and are now successfully deployed in commercial applications.
However, three major gaps exist in the current spoken dialog systems:                1. Each data-driven component of a SDS is trained using independent sources of data to optimize component specific objective functions that are not directly related to performance of a system as a whole, resulting in suboptimal system performance. For example, an objective function for ASR is related to word or utterance error rate, and training data consists of labeled speech (usually application independent) to estimate the acoustic models, and text or transcribed speech (usually application dependent) for language model estimation. Semantic classifiers frequently used in call routing applications require corpora of transcribed utterances labeled by a call designation, and are trained to minimize error rate on a specific training corpus in use.        2. With the possible exception of use of reinforcement learning for dialog management, components of the SDS remain static after the system has been deployed, lacking the ability to adapt to a changing environment.        3. Although data-driven methodology has been shown to both lower design costs and lead to a superior performance for components, the drawback of the statistical approach, and a major inhibitor to progress, is the difficulty in obtaining suitable training data. The cost of data collection depends on a level of annotation required for training. A key research issue concerns finding robust ways to learn structure from un-annotated and partially annotated data. Usually only limited data collection and labeling prior to application deployment is feasible and commercially justifiable.        